Senior Machine Learning Engineer - Computer Vision

Who We Are

We exist to unlock human potential.

Too often, AI drains it—drains budgets, drains energy resources, drains ownership of data. OpenTeams was founded to change that. We build AI that empowers. Our models are energy-efficient, cost-effective, and fully yours. 

Our ethos is open source. That means freedom, trust, and accountability are built into every line of code. We reinvest 3% of our profits back into the open-source community, because we believe tech is most powerful when it serves everyone.

At our core, we value freedom, teamwork, accountability, and uncompromising quality. If you want to fight Goliath, and shape tools that set people free, OpenTeams is the place to do it.

Job Title: Senior Machine Learning Engineer (Computer Vision) 

Location: Remote U.S.

Work Authorization: Must be authorized to work in the United States

About the Role

We're seeking an experienced Computer Vision Engineer who excels at the intersection of computer vision and robust software engineering. In this role, you'll build object detection systems, collaborate with cross-functional partners, and contribute to the technical vision for our computer vision initiatives to ensure our products and solutions are reliable, efficient, and impactful for our clients.

This role is perfect for engineers who are passionate about solving complex computer vision challenges and have strong experience deploying object detection models in production. You'll build detection systems that work across a wide range of infrastructure—from bespoke enterprise stacks to modern open solutions—working collaboratively in a supportive environment that values diverse perspectives and unconventional backgrounds.

What You'll Do

• Guide the design, development, and deployment of scalable, robust, and efficient object detection systems, from data annotation and model training to inference serving and monitoring.
• Develop and implement comprehensive monitoring frameworks to track object detection model performance, identify accuracy degradation and inference bottlenecks, and ensure optimal performance in production environments.
• Collaborate closely with Data Scientists, Computer Vision Researchers, Infrastructure teams, and clients to translate complex detection requirements into robust and impactful ML solutions.
• Optimize object detection pipelines for accuracy, speed, and resource efficiency across diverse deployment scenarios (cloud, edge, specialized hardware).
• Mentor and support other engineers on the team, fostering their professional growth and promoting a culture of technical excellence, knowledge sharing, and continuous learning.
• Maintain clear and thorough documentation to support knowledge sharing and future development.
• Design scalable model serving solutions for both custom-trained detection models and foundation vision models.

 

What We’re Looking For

We encourage you to apply even if you don't meet every qualification listed. We value diverse perspectives and unconventional backgrounds.

• Strong experience deploying, managing, and monitoring object detection models in production environments
• Proficiency in Python and computer vision frameworks (PyTorch, TensorFlow, YOLO, Detectron2, etc.)
• Understanding of object detection architectures (YOLO, Faster R-CNN, RetinaNet, DETR, etc.) and their trade-offs
• Experience owning computer vision systems or products and understanding of the detection model lifecycle
• Technical leadership experience or demonstrated leadership potential, especially in guiding teams and collaborative technical projects
• Experience with image preprocessing, data augmentation, and annotation workflows for object detection
• Experience optimizing detection models for inference speed and accuracy
• Strong written and verbal communication skills and ability to collaborate effectively across multidisciplinary teams

Bonus Points (Not required, but nice to have)

• Experience with full motion video (FMV) processing and object tracking in video streams
• Familiarity with motion detection, multi-object tracking (MOT), or temporal modeling techniques
• Experience with model optimization techniques (quantization, pruning, TensorRT, ONNX)
• Background in edge deployment or real-time detection systems
• Experience with specialized computer vision hardware (GPUs, TPUs, custom accelerators)
• Familiarity with active learning or human-in-the-loop annotation workflows
• Experience leading or mentoring remote teams
• Previous experience contributing to open source computer vision, MLOps,